College Town Natural Experimetns
The Mary Janes
April, 2017
Introduction
Consumer Behavior in Pullman, Washington
Start by looking at quantity and mix of products sold in Pullman, comparing the school year and summertime.
Overall total items sold
Percent of sales that are extracts for inhalation
Statewide and Other City Trends
## Warning: Removed 1 rows containing non-finite values (stat_smooth).## Warning: Removed 1 rows containing missing values (geom_point).
Percent of products sold that are edibles
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## Warning: Removed 1 rows containing missing values (geom_point).
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## Warning: Removed 1 rows containing non-finite values (stat_smooth).
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## Warning: Removed 1 rows containing missing values (geom_point).
Perc Product by Big City, Noncollege-Nonurban, and Pullman
## Warning: Removed 1 rows containing non-finite values (stat_smooth).## Warning: Removed 1 rows containing missing values (geom_point).## Warning: Removed 1 rows containing non-finite values (stat_smooth).## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing non-finite values (stat_smooth).## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing non-finite values (stat_smooth).## Warning: Removed 1 rows containing missing values (geom_point).
##
## Call:
## lm(formula = perc.usable ~ college + beforeSummer + college_before,
## data = did_pullman_seattle)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.043052 -0.019305 0.002384 0.015831 0.049189
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.719350 0.009524 75.529 <2e-16 ***
## college 0.005346 0.013469 0.397 0.695
## beforeSummer -0.018644 0.013469 -1.384 0.179
## college_before 0.026223 0.019048 1.377 0.181
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0252 on 24 degrees of freedom
## Multiple R-squared: 0.1997, Adjusted R-squared: 0.09965
## F-statistic: 1.996 on 3 and 24 DF, p-value: 0.1414##
## Call:
## lm(formula = tot_items ~ college + beforeSummer + college_before,
## data = did_pullman_seattle)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4452.1 -341.6 -64.6 259.6 5245.9
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 16589.1 909.3 18.244 1.42e-15 ***
## college -15836.7 1285.9 -12.316 7.29e-12 ***
## beforeSummer 3317.0 1285.9 2.580 0.0164 *
## college_before -3314.3 1818.5 -1.822 0.0809 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2406 on 24 degrees of freedom
## Multiple R-squared: 0.9401, Adjusted R-squared: 0.9326
## F-statistic: 125.6 on 3 and 24 DF, p-value: 8.321e-15##
## Call:
## lm(formula = perc.extracts ~ college + beforeSummer + college_before,
## data = did_pullman_seattle)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0238026 -0.0033981 -0.0000447 0.0036002 0.0298770
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.111405 0.004454 25.014 < 2e-16 ***
## college -0.014757 0.006298 -2.343 0.027751 *
## beforeSummer 0.001647 0.006298 0.261 0.795940
## college_before 0.036798 0.008907 4.131 0.000378 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01178 on 24 degrees of freedom
## Multiple R-squared: 0.6129, Adjusted R-squared: 0.5645
## F-statistic: 12.67 on 3 and 24 DF, p-value: 3.659e-05##
## Call:
## lm(formula = perc.edible ~ college + beforeSummer + college_before,
## data = did_pullman_seattle)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.033336 -0.015386 -0.002612 0.013170 0.033015
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.131097 0.007554 17.356 4.36e-15 ***
## college -0.018924 0.010682 -1.772 0.0892 .
## beforeSummer 0.011427 0.010682 1.070 0.2954
## college_before -0.039443 0.015107 -2.611 0.0153 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01998 on 24 degrees of freedom
## Multiple R-squared: 0.5876, Adjusted R-squared: 0.5361
## F-statistic: 11.4 on 3 and 24 DF, p-value: 7.669e-05##
## Call:
## lm(formula = perc.usable ~ college + beforeSummer + college_before,
## data = did_pullman_spokane)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.043052 -0.012561 0.001369 0.013180 0.049189
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.716946 0.008565 83.705 <2e-16 ***
## college 0.015329 0.012113 1.266 0.218
## beforeSummer 0.009995 0.012113 0.825 0.417
## college_before -0.017574 0.017130 -1.026 0.315
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02266 on 24 degrees of freedom
## Multiple R-squared: 0.06454, Adjusted R-squared: -0.0524
## F-statistic: 0.5519 on 3 and 24 DF, p-value: 0.6518##
## Call:
## lm(formula = tot_items ~ college + beforeSummer + college_before,
## data = did_pullman_spokane)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1158.43 -464.46 -91.29 114.61 1739.57
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7017.4 299.9 23.396 < 2e-16 ***
## college -6262.3 424.2 -14.763 1.53e-13 ***
## beforeSummer -992.9 424.2 -2.341 0.0279 *
## college_before 990.1 599.9 1.651 0.1119
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 793.6 on 24 degrees of freedom
## Multiple R-squared: 0.9399, Adjusted R-squared: 0.9324
## F-statistic: 125.1 on 3 and 24 DF, p-value: 8.738e-15##
## Call:
## lm(formula = perc.extracts ~ college + beforeSummer + college_before,
## data = did_pullman_spokane)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.023803 -0.010274 -0.002200 0.008437 0.029877
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.168860 0.005192 32.521 < 2e-16 ***
## college -0.033767 0.007343 -4.598 0.000115 ***
## beforeSummer 0.003008 0.007343 0.410 0.685678
## college_before -0.041454 0.010385 -3.992 0.000538 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01374 on 24 degrees of freedom
## Multiple R-squared: 0.8516, Adjusted R-squared: 0.8331
## F-statistic: 45.91 on 3 and 24 DF, p-value: 4.27e-10##
## Call:
## lm(formula = perc.edible ~ college + beforeSummer + college_before,
## data = did_pullman_spokane)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.033336 -0.005723 -0.000689 0.004758 0.028886
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.075373 0.005560 13.555 9.64e-13 ***
## college 0.008785 0.007864 1.117 0.27500
## beforeSummer -0.005670 0.007864 -0.721 0.47786
## college_before 0.033685 0.011121 3.029 0.00579 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01471 on 24 degrees of freedom
## Multiple R-squared: 0.5894, Adjusted R-squared: 0.5381
## F-statistic: 11.48 on 3 and 24 DF, p-value: 7.285e-05## [1] "collegetown" "saledate" "revenue"
## [4] "tot_items" "avg_price_peritem" "perc.usable"
## [7] "perc.edible" "perc.extracts"##
## Call:
## lm(formula = perc.usable ~ college + beforeSummer + college_before,
## data = did_pullman_noncollege)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.043052 -0.014397 0.001367 0.013006 0.049189
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.714375 0.008759 81.558 <2e-16 ***
## college 0.017900 0.012387 1.445 0.161
## beforeSummer 0.016998 0.012387 1.372 0.183
## college_before -0.024577 0.017518 -1.403 0.173
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02317 on 24 degrees of freedom
## Multiple R-squared: 0.1, Adjusted R-squared: -0.01246
## F-statistic: 0.8893 on 3 and 24 DF, p-value: 0.4608
##
## Call:
## lm(formula = perc.extracts ~ college + beforeSummer + college_before,
## data = did_pullman_noncollege)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0238026 -0.0039328 0.0001344 0.0046867 0.0298770
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1415556 0.0044484 31.821 < 2e-16 ***
## college -0.0064627 0.0062910 -1.027 0.314526
## beforeSummer -0.0007699 0.0062910 -0.122 0.903618
## college_before -0.0376756 0.0088969 -4.235 0.000291 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01177 on 24 degrees of freedom
## Multiple R-squared: 0.7439, Adjusted R-squared: 0.7119
## F-statistic: 23.24 on 3 and 24 DF, p-value: 2.804e-07
##
## Call:
## lm(formula = perc.edible ~ college + beforeSummer + college_before,
## data = did_pullman_noncollege)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.033336 -0.010212 -0.003450 0.008487 0.028886
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.107721 0.006312 17.067 6.33e-15 ***
## college -0.023563 0.008926 -2.640 0.01435 *
## beforeSummer -0.008578 0.008926 -0.961 0.34612
## college_before 0.036594 0.012623 2.899 0.00788 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0167 on 24 degrees of freedom
## Multiple R-squared: 0.3234, Adjusted R-squared: 0.2388
## F-statistic: 3.824 on 3 and 24 DF, p-value: 0.02268
##
## Call:
## lm(formula = perc.usable ~ college + fallSemester + college_fall,
## data = did_pullman_noncollege_fallsem)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.053011 -0.011930 0.001334 0.009271 0.048185
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.715931 0.008923 80.233 < 2e-16 ***
## college 0.046743 0.012619 3.704 0.00111 **
## fallSemester 0.003667 0.012619 0.291 0.77384
## college_fall -0.034699 0.017846 -1.944 0.06367 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02361 on 24 degrees of freedom
## Multiple R-squared: 0.4144, Adjusted R-squared: 0.3412
## F-statistic: 5.661 on 3 and 24 DF, p-value: 0.004439##
## Call:
## lm(formula = perc.extracts ~ college + fallSemester + college_fall,
## data = did_pullman_noncollege_fallsem)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.021738 -0.005143 -0.000400 0.003117 0.032301
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.142761 0.004147 34.428 < 2e-16 ***
## college -0.020774 0.005864 -3.543 0.00166 **
## fallSemester 0.001342 0.005864 0.229 0.82089
## college_fall 0.023657 0.008293 2.853 0.00879 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01097 on 24 degrees of freedom
## Multiple R-squared: 0.488, Adjusted R-squared: 0.4241
## F-statistic: 7.626 on 3 and 24 DF, p-value: 0.0009481##
## Call:
## lm(formula = perc.edible ~ college + fallSemester + college_fall,
## data = did_pullman_noncollege_fallsem)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.034076 -0.009247 -0.000059 0.010763 0.041184
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.097600 0.006544 14.914 1.23e-13 ***
## college -0.012304 0.009255 -1.329 0.196
## fallSemester -0.009509 0.009255 -1.027 0.314
## college_fall 0.019212 0.013088 1.468 0.155
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01731 on 24 degrees of freedom
## Multiple R-squared: 0.08831, Adjusted R-squared: -0.02565
## F-statistic: 0.7749 on 3 and 24 DF, p-value: 0.5194Noncollege, Non Urban
##
## Call:
## lm(formula = perc.usable ~ college + beforeSummer + college_before,
## data = did_pullman_noncollege_nonurban)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.043052 -0.013132 0.002439 0.012509 0.049189
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.742694 0.008523 87.139 <2e-16 ***
## college -0.010419 0.012053 -0.864 0.396
## beforeSummer 0.015568 0.012053 1.292 0.209
## college_before -0.023147 0.017046 -1.358 0.187
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02255 on 24 degrees of freedom
## Multiple R-squared: 0.2666, Adjusted R-squared: 0.1749
## F-statistic: 2.907 on 3 and 24 DF, p-value: 0.05533##
## Call:
## lm(formula = perc.edible ~ college + beforeSummer + college_before,
## data = did_pullman_noncollege_nonurban)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.033336 -0.008104 -0.003345 0.009915 0.028886
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.089731 0.005983 14.998 1.09e-13 ***
## college -0.005573 0.008461 -0.659 0.51636
## beforeSummer -0.007654 0.008461 -0.905 0.37465
## college_before 0.035670 0.011966 2.981 0.00649 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01583 on 24 degrees of freedom
## Multiple R-squared: 0.3997, Adjusted R-squared: 0.3247
## F-statistic: 5.327 on 3 and 24 DF, p-value: 0.005884##
## Call:
## lm(formula = perc.extracts ~ college + beforeSummer + college_before,
## data = did_pullman_noncollege_nonurban)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0238026 -0.0037265 -0.0008578 0.0052419 0.0298770
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.136883 0.004469 30.629 < 2e-16 ***
## college -0.001790 0.006320 -0.283 0.779407
## beforeSummer -0.002769 0.006320 -0.438 0.665222
## college_before -0.035677 0.008938 -3.991 0.000538 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01182 on 24 degrees of freedom
## Multiple R-squared: 0.7018, Adjusted R-squared: 0.6645
## F-statistic: 18.83 on 3 and 24 DF, p-value: 1.696e-06##
## Call:
## lm(formula = avg_price_peritem ~ college + beforeSummer + college_before,
## data = did_pullman_noncollege_nonurban)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.11647 -0.45267 0.08998 0.70174 1.59286
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 24.2816 0.3879 62.604 < 2e-16 ***
## college 3.7463 0.5485 6.830 4.6e-07 ***
## beforeSummer 0.3974 0.5485 0.724 0.476
## college_before -0.2763 0.7757 -0.356 0.725
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.026 on 24 degrees of freedom
## Multiple R-squared: 0.784, Adjusted R-squared: 0.757
## F-statistic: 29.04 on 3 and 24 DF, p-value: 3.718e-08##
## Call:
## lm(formula = avg_price_peritem ~ college + beforeSummer + college_before,
## data = did_pullman_noncollege_nonurban)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.11647 -0.45267 0.08998 0.70174 1.59286
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 24.2816 0.3879 62.604 < 2e-16 ***
## college 3.7463 0.5485 6.830 4.6e-07 ***
## beforeSummer 0.3974 0.5485 0.724 0.476
## college_before -0.2763 0.7757 -0.356 0.725
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.026 on 24 degrees of freedom
## Multiple R-squared: 0.784, Adjusted R-squared: 0.757
## F-statistic: 29.04 on 3 and 24 DF, p-value: 3.718e-08##
## Call:
## lm(formula = tot_items ~ college + beforeSummer + college_before,
## data = did_pullman_noncollege_nonurban)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6525.4 -1177.2 -104.4 114.6 8020.6
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 28286 1311 21.578 < 2e-16 ***
## college -27531 1854 -14.850 1.35e-13 ***
## beforeSummer -3956 1854 -2.134 0.0433 *
## college_before 3953 2622 1.508 0.1447
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3468 on 24 degrees of freedom
## Multiple R-squared: 0.9413, Adjusted R-squared: 0.9339
## F-statistic: 128.2 on 3 and 24 DF, p-value: 6.617e-15##
## Call:
## lm(formula = revenue ~ college + beforeSummer + college_before,
## data = did_pullman_noncollege_nonurban)
##
## Residuals:
## Min 1Q Median 3Q Max
## -152413 -48210 -2171 5381 222191
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 686982 33927 20.249 < 2e-16 ***
## college -665779 47980 -13.876 5.84e-13 ***
## beforeSummer -85085 47980 -1.773 0.0889 .
## college_before 85112 67854 1.254 0.2218
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 89760 on 24 degrees of freedom
## Multiple R-squared: 0.9342, Adjusted R-squared: 0.9259
## F-statistic: 113.5 on 3 and 24 DF, p-value: 2.586e-14Fall Semester
##
## Call:
## lm(formula = perc.usable ~ college + fallSemester + college_fall,
## data = did_fallsem_noncollege_nonurban)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.053011 -0.008539 0.001830 0.007296 0.048185
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.720918 0.008684 83.018 < 2e-16 ***
## college 0.041756 0.012281 3.400 0.00236 **
## fallSemester 0.002770 0.012281 0.226 0.82345
## college_fall -0.033801 0.017368 -1.946 0.06343 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02298 on 24 degrees of freedom
## Multiple R-squared: 0.3787, Adjusted R-squared: 0.301
## F-statistic: 4.876 on 3 and 24 DF, p-value: 0.008697##
## Call:
## lm(formula = perc.edible ~ college + fallSemester + college_fall,
## data = did_fallsem_noncollege_nonurban)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.034076 -0.009811 -0.000522 0.007139 0.041184
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.087482 0.006345 13.787 6.71e-13 ***
## college -0.002186 0.008974 -0.244 0.810
## fallSemester -0.007769 0.008974 -0.866 0.395
## college_fall 0.017472 0.012691 1.377 0.181
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01679 on 24 degrees of freedom
## Multiple R-squared: 0.1106, Adjusted R-squared: -0.0005933
## F-statistic: 0.9947 on 3 and 24 DF, p-value: 0.4122##
## Call:
## lm(formula = perc.extracts ~ college + fallSemester + college_fall,
## data = did_fallsem_noncollege_nonurban)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.021738 -0.004491 -0.000549 0.003308 0.032301
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1507051 0.0041289 36.500 < 2e-16 ***
## college -0.0287187 0.0058392 -4.918 5.11e-05 ***
## fallSemester 0.0001914 0.0058392 0.033 0.97412
## college_fall 0.0248075 0.0082579 3.004 0.00615 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01092 on 24 degrees of freedom
## Multiple R-squared: 0.5858, Adjusted R-squared: 0.534
## F-statistic: 11.31 on 3 and 24 DF, p-value: 8.073e-05##
## Call:
## lm(formula = avg_price_peritem ~ college + fallSemester + college_fall,
## data = did_fallsem_noncollege_nonurban)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.1992 -0.4471 0.1483 0.4487 1.9141
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 24.7673 0.3971 62.371 < 2e-16 ***
## college -3.9935 0.5616 -7.111 2.38e-07 ***
## fallSemester -1.0184 0.5616 -1.814 0.0823 .
## college_fall 1.4477 0.7942 1.823 0.0808 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.051 on 24 degrees of freedom
## Multiple R-squared: 0.7491, Adjusted R-squared: 0.7178
## F-statistic: 23.89 on 3 and 24 DF, p-value: 2.194e-07##
## Call:
## lm(formula = tot_items ~ college + fallSemester + college_fall,
## data = did_fallsem_noncollege_nonurban)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7111.0 -1652.5 -117.5 145.2 10587.0
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 39166 1491 26.276 < 2e-16 ***
## college -38061 2108 -18.056 1.8e-15 ***
## fallSemester 2523 2108 1.197 0.243
## college_fall -2170 2981 -0.728 0.474
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3944 on 24 degrees of freedom
## Multiple R-squared: 0.9664, Adjusted R-squared: 0.9622
## F-statistic: 230.4 on 3 and 24 DF, p-value: < 2.2e-16##
## Call:
## lm(formula = revenue ~ college + fallSemester + college_fall,
## data = did_fallsem_noncollege_nonurban)
##
## Residuals:
## Min 1Q Median 3Q Max
## -168695 -43576 -3037 2588 260398
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 969082 36849 26.299 < 2e-16 ***
## college -946120 52112 -18.155 1.59e-15 ***
## fallSemester 22211 52112 0.426 0.674
## college_fall -14259 73698 -0.193 0.848
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 97490 on 24 degrees of freedom
## Multiple R-squared: 0.9654, Adjusted R-squared: 0.9611
## F-statistic: 223.1 on 3 and 24 DF, p-value: < 2.2e-16Noncollege, Non Urban
##
## Call:
## lm(formula = perc.usable ~ college + beforeSummer + college_before,
## data = did_pullman_noncollege_nonurban)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.043052 -0.013132 0.002439 0.012509 0.049189
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.742694 0.008523 87.139 <2e-16 ***
## college -0.010419 0.012053 -0.864 0.396
## beforeSummer 0.015568 0.012053 1.292 0.209
## college_before -0.023147 0.017046 -1.358 0.187
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02255 on 24 degrees of freedom
## Multiple R-squared: 0.2666, Adjusted R-squared: 0.1749
## F-statistic: 2.907 on 3 and 24 DF, p-value: 0.05533##
## Call:
## lm(formula = perc.edible ~ college + beforeSummer + college_before,
## data = did_pullman_noncollege_nonurban)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.033336 -0.008104 -0.003345 0.009915 0.028886
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.089731 0.005983 14.998 1.09e-13 ***
## college -0.005573 0.008461 -0.659 0.51636
## beforeSummer -0.007654 0.008461 -0.905 0.37465
## college_before 0.035670 0.011966 2.981 0.00649 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01583 on 24 degrees of freedom
## Multiple R-squared: 0.3997, Adjusted R-squared: 0.3247
## F-statistic: 5.327 on 3 and 24 DF, p-value: 0.005884##
## Call:
## lm(formula = perc.extracts ~ college + beforeSummer + college_before,
## data = did_pullman_noncollege_nonurban)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0238026 -0.0037265 -0.0008578 0.0052419 0.0298770
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.136883 0.004469 30.629 < 2e-16 ***
## college -0.001790 0.006320 -0.283 0.779407
## beforeSummer -0.002769 0.006320 -0.438 0.665222
## college_before -0.035677 0.008938 -3.991 0.000538 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01182 on 24 degrees of freedom
## Multiple R-squared: 0.7018, Adjusted R-squared: 0.6645
## F-statistic: 18.83 on 3 and 24 DF, p-value: 1.696e-06##
## Call:
## lm(formula = avg_price_peritem ~ college + beforeSummer + college_before,
## data = did_pullman_noncollege_nonurban)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.11647 -0.45267 0.08998 0.70174 1.59286
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 24.2816 0.3879 62.604 < 2e-16 ***
## college 3.7463 0.5485 6.830 4.6e-07 ***
## beforeSummer 0.3974 0.5485 0.724 0.476
## college_before -0.2763 0.7757 -0.356 0.725
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.026 on 24 degrees of freedom
## Multiple R-squared: 0.784, Adjusted R-squared: 0.757
## F-statistic: 29.04 on 3 and 24 DF, p-value: 3.718e-08##
## Call:
## lm(formula = avg_price_peritem ~ college + beforeSummer + college_before,
## data = did_pullman_noncollege_nonurban)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.11647 -0.45267 0.08998 0.70174 1.59286
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 24.2816 0.3879 62.604 < 2e-16 ***
## college 3.7463 0.5485 6.830 4.6e-07 ***
## beforeSummer 0.3974 0.5485 0.724 0.476
## college_before -0.2763 0.7757 -0.356 0.725
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.026 on 24 degrees of freedom
## Multiple R-squared: 0.784, Adjusted R-squared: 0.757
## F-statistic: 29.04 on 3 and 24 DF, p-value: 3.718e-08##
## Call:
## lm(formula = tot_items ~ college + beforeSummer + college_before,
## data = did_pullman_noncollege_nonurban)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6525.4 -1177.2 -104.4 114.6 8020.6
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 28286 1311 21.578 < 2e-16 ***
## college -27531 1854 -14.850 1.35e-13 ***
## beforeSummer -3956 1854 -2.134 0.0433 *
## college_before 3953 2622 1.508 0.1447
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3468 on 24 degrees of freedom
## Multiple R-squared: 0.9413, Adjusted R-squared: 0.9339
## F-statistic: 128.2 on 3 and 24 DF, p-value: 6.617e-15##
## Call:
## lm(formula = revenue ~ college + beforeSummer + college_before,
## data = did_pullman_noncollege_nonurban)
##
## Residuals:
## Min 1Q Median 3Q Max
## -152413 -48210 -2171 5381 222191
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 686982 33927 20.249 < 2e-16 ***
## college -665779 47980 -13.876 5.84e-13 ***
## beforeSummer -85085 47980 -1.773 0.0889 .
## college_before 85112 67854 1.254 0.2218
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 89760 on 24 degrees of freedom
## Multiple R-squared: 0.9342, Adjusted R-squared: 0.9259
## F-statistic: 113.5 on 3 and 24 DF, p-value: 2.586e-14